In this paper, we propose a Multi-View Transformer (MVT) for 3D visual grounding to learn such a view-robust representation. Given the 3D scene under a specific view, our MVT projects it to a multi-view space by rotating the original view with equal angles. Our approach simultaneously models the position information within the 3D scene under different views and aggregates all information to learn a view-robust representation by eliminating the dependence on the starting view. Furthermore, to reduce the computational cost of modeling multi-view space, we decouple the 3D object representation computation into computing point clouds features and object coordinates separately to share the point clouds features across views. Besides, with the rich and fine-grained category labels provided by Nr3D [1]} and Sr3D [1]}, we make full use of these language priors and propose a language-guided object classification task to enhance the object encoder further.
Extensive experiments have been conducted on three mainstream benchmark datasets Nr3D [1]}, Sr3D [1]}, and ScanRefer [5]}. Our MVT outperforms all state-of-the-art methods by a large margin. Specifically, on Nr3D and Sr3D, MVT not only outperforms the best competitor by \(11.2\%\)  and \(7.1\%\)  respectively but also surpasses the method [6]} with extra 2D assistance by \(5.9\%\)  and \(6.6\%\) .
